SpatialFinder: Framework for Prioritizing High-Value Regions in Spatial Transcriptomics

A 113.8 KB PDF authored by Jonathan Xu and last updated on April 15, 2026, describes the SpatialFinder framework. This framework combines a biomedical vision-language model with a human-in-the-loop pipeline to predict gene expression heterogeneity from H&E tissue slides. It aims to make spatial transcriptomics more cost-effective by identifying smaller, high-value regions of interest for sequencing.

Use Cases

• Rank high-value regions of interest (ROIs) for spatial transcriptomics sequencing based on predicted gene expression heterogeneity.
• Prioritize tumor-targeted microenvironments from standard H&E tissue slides using a vision-language model.
• Identify diverse tissue regions for analysis to capture biological variation mentioned in the description.
• Apply a human-in-the-loop optimization pipeline to tailor ROI selection to specific clinical interests.

Strengths

• Framework evaluated across four Visium HD tissue types, suggesting multi-tissue validation.
• Achieved performance metrics including Spearman's ρ up to 0.89 and Overlap@10% up to 78.8%.
• Released under a permissive CC-BY-4.0 license for open use and modification.

Limitations

• The dataset is a 113.8 KB PDF describing a framework; the underlying data or model outputs are not directly provided.
• Column-level documentation and sample data are unavailable, limiting immediate suitability assessment.
• Row count and specific data formats for the framework's inputs/outputs are unknown.

Provenance

Source

figshare

Collection Method

Describes a computational framework (SpatialFinder) combining a vision-language model with human-in-the-loop optimization.

Time Range

Publication date is implied as 2026.

Freshness

Last updated 2026-04-15 05:22:57.

Geography

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